Scientific hypothesis generation is central to discovery, enabling researchers to propose ideas, design experiments, and validate knowledge. Yet, producing hypotheses that are both novel and empirically grounded remains challenging. Traditional methods rely heavily on human intuition, while automated approaches often lack scientific rigor and meaningful validation. This work presents the Monte Carlo Self-Refine Tree (MC-NEST), a general-purpose framework that automates hypothesis generation by integrating Monte Carlo Tree Search (MCTS) with adaptive sampling and iterative self-evaluation. MC-NEST treats hypothesis generation as a structured search problem, dynamically balancing exploration and refinement through strategy selection. We evaluate MC-NEST on a benchmark spanning biomedicine, social science, and computer science. Experimental results show that MC-NEST consistently outperforms state-of-the-art prompt-based baselines across four human-annotated criteria: novelty, clarity, significance, and verifiability. Specifically, it achieves average scores of 2.65, 2.74, and 2.80 in social science, computer science, and biomedicine, respectively, outperforming baseline scores of 2.36, 2.51, and 2.52. These findings highlight MC-NEST’s ability to generate interpretable, scientifically meaningful hypotheses across domains. Furthermore, MC-NEST is designed to support human-AI collaboration through its interpretable tree structure, enabling future integration where domain experts can guide exploration and validate hypotheses transparently and reproducibly. By integrating strategic search, self-refinement, and adaptive validation, MC-NEST offers a scalable and effective path toward responsible, automated scientific discovery.

Abstract Generating context-aware, high-quality, and innovative scientific ideas remains a central challenge in AI-supported research. We introduce SCI-IDEA , a two-stage framework combining large language models (LLMs) with a specialised Aha-Moment Detection module for iterative idea refinement. The first stage extracts structured facets: objectives, methodology, evaluation, and future work from previous publications, compressing each paper to $${\sim }$$ 200 tokens to enable scalable processing of researcher profiles that would otherwise exceed frontier LLM context windows. The second stage integrates these facets with a token-level embedding approach to identify novelty and context gaps, indicating candidate ideas as Aha moments when they surpass predefined thresholds for both novelty and surprise. We evaluate SCI-IDEA across 100 computer-science researcher profiles, 4 LLMs (GPT-4o, GPT−4.5, DeepSeek-32B, DeepSeek-70B), three embedding strategies, and 5 prompting configurations via a hybrid protocol of automated LLM-as-judge scoring with GPT−4.1 and a human evaluation with 15 PhD-level domain experts. These two evaluation signals diverge substantially: LLM-based scores exceed expert ratings by 3–4 points on a 10-point scale, with near-zero inter-rater correlation ( $$r = 0.02$$ –0.17, $$p > 0.05$$ ), indicating that automated scores reflect relative architectural comparisons rather than human-verified measures of absolute idea quality. Within the LLM-evaluated setting, SCI-IDEA with token-level embeddings achieves a mean quality score of 6.97, a modest but constant improvement over the LLM-only baseline ( $$\Delta = +0.20$$ points; $$p < 0.05$$ ) and driven primarily by improved feasibility scores ( $$+0.21$$ points, $$p < 0.001$$ ). Expert evaluators independently confirmed this trend while assigning lower absolute scores. These findings suggest an architectural advantage of facet-based context modelling and token-level novelty detection, though their practical importance deserves validation through longitudinal studies with larger domain experts. This work is validated in the computer science domain, but its application to other fields requires further investigation.

The rapid development of generative AI leveraging neural models, particularly with the introduction of large language models (LLMs), has fundamentally advanced natural language processing and generation. However, such neural models are non-deterministic, opaque, and tend to confabulate. Knowledge Graphs (KGs) on the other hand contain factual information represented in a symbolic way for humans and machines following formal knowledge representation formalisms. However, the creation and curation of KGs is time-consuming, cumbersome, and resource-demanding. A key research challenge now is how to synergistically combine both formalisms with the human in the loop (Hybrid AI) to obtain structured and machine-processable knowledge in a scalable way. We introduce an approach for a tight integration of Humans, Neural Models (LLM), and Symbolic Representations (KG) for the semiautomatic creation and curation of Scholarly Knowledge Graphs. Our approach, while demonstrated in the scholarly context, establishes generalizable principles for neuro-symbolic integration that can be adapted to other domains. We implement and integrate our approach comprising an intelligent user interface and prompt templates for interaction with an LLM in the Open Research Knowledge Graph. We perform a thorough analysis of our approach and implementation with a user evaluation to assess the merits of the neuro-symbolic, hybrid approach for organizing scholarly knowledge.

In recent developments of AI, we see an increasing augmentation of neural models with unstructured data (RAG), structured data (Graph RAG) and even agents, reasoning services and APIs (Agentic AI). In the evolving landscape of scientific knowledge management, the integration of neuro-symbolic and agentic AI approaches offers opportunities to enhance the organization, discovery, and synthesis of research contributions. We explore how knowledge graphs and large language models (LLMs) can be synergistically combined to advance the representation and accessibility of scholarly knowledge. At the heart of this approach is the Open Research Knowledge Graph (ORKG)—a platform that structures scientific knowledge into machine-readable representations, enabling comparative analyses, automated reasoning, and contextualized exploration of research findings. Extending this vision, ORKG ASK introduces a novel query and synthesis system, combining symbolic knowledge with neural AI capabilities to provide precise, explainable, and interactive responses to complex scientific inquiries. We will examine the foundations and practical applications of agentic and neuro-symbolic AI in scholarly knowledge organization. By bridging the gap between symbolic representations, agentic tools and neural models, this approach aims to make scientific knowledge more accessible, transparent, and actionable—paving the way for a new era of AI-driven research assistance.

Effective human-AI interaction relies on AI's ability to accurately perceive and interpret human emotions. Current benchmarks for vision and vision-language models are severely limited, offering a narrow emotional spectrum that overlooks nuanced states (e.g., bitterness, intoxication) and fails to distinguish subtle differences between related feelings (e.g., shame vs. embarrassment). Existing datasets also often use uncontrolled imagery with occluded faces and lack demographic diversity, risking significant bias. To address these critical gaps, we introduce EmoNet Face, a comprehensive benchmark suite. EmoNet Face features: (1) A novel 40-category emotion taxonomy, meticulously derived from foundational research to capture finer details of human emotional experiences. (2) Three large-scale, AI-generated datasets (EmoNet HQ, Binary, and Big) with explicit, full-face expressions and controlled demographic balance across ethnicity, age, and gender. (3) Rigorous, multi-expert annotations for training and high-fidelity evaluation. (4) We build Empathic Insight Face, a model achieving human-expert-level performance on our benchmark. The publicly released EmoNet Face suite—taxonomy, datasets, and model—provides a robust foundation for developing and evaluating AI systems with a deeper understanding of human emotions.

In this paper, we propose a Retrieval-Augmented Generation-based (RAG) approach for fair access to food data in developing countries. This work also contributes to achieving sustainable development goals, specifically goal 2: Zero hunger and goal 3: Ensure healthy lives and promote well-being for all at all ages. Actually, given that a lot of African food data is accessible only in PDF format, we firstly extracted and organized these data using the Open Research Knowledge Graph (ORKG) and the image data using the Firebase database, with the link to the corresponding food description in the ORKG. Currently, more than 1000 foods eaten in around 50 African countries are already documented. Given that Large Language Models necessitate a lot of data and resources to be train/fine-tuned, which we do not have in our university, our idea is to use the food data stored in the ORKG to build a Retrieval-Augmented Generation system (RAG) for improving access to food data in Africa. Its implementation involved the use of multi-embedding models, vector databases (Pinecone), Falcon3 and LangChain. Because we do not have enough resources, this work is made possible thanks to a collaboration with TIB-Hannover who provide access to remote computers.

Academic Search Engines (ASEs) are crucial for navigating the vast landscape of scholarly literature. Traditionally, these engines rely on keyword-based search, supplemented by predefined facets encompassing metadata such as research field, publication year, type, authors, and language. However, ASEs are limited in their ability to generate dynamic facets in real-time based on article contents. This limitation impedes the efficient exploration and navigation of large article collections. We propose an approach that addresses this limitation by dynamically generating facets using article abstracts. We introduce three distinct methods for dynamic facet generation: (1) KB2 (based on Knowledge Bases) utilizes two knowledge bases (KB) to extract facet values and their associated facets; (2) KBLLM (based on a Knowledge Base and a Large Language Model) utilizes a KB for extracting facet values and a large language model (LLM) to categorize these values by predicting facets; finally, (3) KBLLMKA (based on a Knowledge Base and a Large Language Model with Knowledge Augmentation) combines KB-spotting with facet-value pair extraction and adds this information as auxiliary data to enhance LLM’s facet prediction capabilities. We evaluated the effectiveness of these methods with a user study, performance evaluation, and comparative analyses, which showed the effectiveness of the approach.

[Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its “current” state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020–2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000–2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.

In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend Plumber, a framework that brings together the research community's disjoint efforts on KG completion. We include more components into the architecture of Plumber to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, Plumber dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of Plumber for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of Plumber in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.

Abstract In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question’s answer by “connecting the dots” across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm. The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions, originating from comprehensive evaluations against nine existing systems, are (1) a case study in which two preference ordering approaches are systematically compared, and where the pointwise approach is shown to outperform the pairwise approach, thus adding to the existing survey of observations on this topic; (2) since our system outperforms a highly-effective TF-IDF-based IR technique by 3.5 and 4.9 points on the development and test sets, respectively, it demonstrates some of the further task improvement possibilities (e.g., in terms of an efficient learning algorithm, semantic features) on this task; (3) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (4) the human-engineered features make it an interpretable machine learning model for the task.

The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.

structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources.

Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.

Abstract With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations. Resource: https://tibonto.github.io/educor/ .

Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.

We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present a state-of-the-art deep learning baseline. Further, we propose the active learning strategy for an optimal selection of instances from among the various domains in our data. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.

Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.

OpenCourseWare (OCW) has become a desirable source for sharing free educational resources which means there will always be users with differing needs. It is therefore the responsibility of OCW platform developers to consider accessibility as one of their prioritized requirements to ensure ease of use for all, including those with disabilities. However, the main challenge when creating an accessible platform is the ability to address all the different types of barriers that might affect those with a wide range of physical, sensory and cognitive impairments. This article discusses accessibility and personalization strategies and their realisation in the SlideWiki platform, in order to facilitate the development of accessible OCW. Previously, accessibility was seen as a complementary feature that can be tackled in the implementation phase. However, a meaningful integration of accessibility features requires thoughtful consideration during all project phases with active involvement of related stakeholders. The evaluation results and lessons learned from the SlideWiki development process have the potential to assist in the development of other systems that aim for an inclusive approach.

In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.

We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.

Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.

Increasing data volumes have extensively increased application possibilities. However, accessing this data in an ad hoc manner remains an unsolved problem due to the diversity of data management approaches, formats and storage frameworks, resulting in the need to effectively access and process distributed heterogeneous data at scale. For years, Semantic Web techniques have addressed data integration challenges with practical knowledge representation models and ontology-based mappings. Leveraging these techniques, we provide a solution enabling uniform access to large, heterogeneous data sources, without enforcing centralization; thus realizing the vision of a Semantic Data Lake. In this paper, we define the core concepts underlying this vision and the architectural requirements that systems implementing it need to fulfill. Squerall, an example of such a system, is an extensible framework built on top of state-of-the-art Big Data technologies. We focus on Squerall's distributed query execution techniques and strategies, empirically evaluating its performance throughout its various sub-phases.

E-learning accessibility is a well studied topic that aims at making online educational resources accessible for everyone, including disabled users. The focus of these efforts is mainly on making content consumption more accessible while content creation is often neglected. This means that the perspectives of disabled users are not accommodated and that full inclusiveness cannot be reached. In this work we introduce a novel interface to author educational content, in particular presentation slides. This interface is specifically designed for visually impaired and blind users. The interface is implemented in the OpenCourseWare platform SlideWiki. Instead of the default visual WYSIWYG interface, the alternative editor uses the lightweight markup language Markdown to support content authoring.

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. In this form, scholarly knowledge is hard to process automatically. We present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing. The primary contribution is to present, evaluate and discuss multi-modal scholarly knowledge acquisition, combining crowdsourced and automated techniques. We present the results of the first user evaluation of the infrastructure with the participants of a recent international conference. Results suggest that users were intrigued by the novelty of the proposed infrastructure and by the possibilities for innovative scholarly knowledge processing it could enable.

Visualizations can support the development, exploration, communication, and sense-making of ontologies. Suitable visualizations, however, are highly dependent on individual use cases and targeted user groups. In this article, we present a methodology that enables customizable definitions for the visual representation of ontologies.

Squerall is a tool that allows the querying of heterogeneous, large-scale data sources by leveraging state-of-the-art Big Data processing engines: Spark and Presto. Queries are posed on-demand against a Data Lake, i.e., directly on the original data sources without requiring prior data transformation. We showcase Squerall's ability to query five different data sources, including inter alia the popular Cassandra and MongoDB. In particular, we demonstrate how it can jointly query heterogeneous data sources, and how interested developers can easily extend it to support additional data sources. Graphical user interfaces (GUIs) are offered to support users in (1) building intra-source queries, and (2) creating required input files.

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.

For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.

Abstract In this article we describe the Linked Data Notifications (LDN) protocol, which is a W3C Candidate Recommendation. Notifications are sent over the Web for a variety of purposes, for example, by social applications. The information contained within a notification is structured arbitrarily, and typically only usable by the application which generated it in the first place. In the spirit of Linked Data, we propose that notifications should be reusable by multiple authorised applications. Through separating the concepts of senders, receivers and consumers of notifications, and leveraging Linked Data principles of shared vocabularies and URIs, LDN provides a building block for decentralised Web applications. This permits end users more freedom to switch between the online tools they use, as well as generating greater value when notifications from different sources can be used in combination. We situate LDN alongside related initiatives, and discuss additional considerations such as security and abuse prevention measures. We evaluate the protocol’s effectiveness by analysing multiple, independent implementations, which pass a suite of formal tests and can be demonstrated interoperating with each other. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/linked-data-notifications .

Interoperability among actors, sensors, and heterogeneous systems is a crucial factor for realizing the Industry 4.0 vision, i.e., the creation of Smart Factories by enabling intelligent human-to-machine and machine-to-machine cooperation. In order to empower interoperability in Smart Factories, standards and reference architectures have been proposed. Standards allow for the description of components, systems, and processes, as well as interactions among them. Reference architectures classify, align, and integrate industrial standards according to their purposes and features. Industrial communities in Europe, the United States, and Asia have proposed various reference architectures. However, interoperability among analogous standards in these reference architectures is hampered due to different granularity representation of similar processes or production parts. In this paper, we survey the landscape of Industry 4.0 standards from a semantic perspective. To tackle the problem of interoperability between standards, we developed STO, an ontology for describing standards and their relations. Characteristics of I4.0 standards are described using STO, and these descriptions are exploited for classifying standards from different perspectives according to the reference architectures. Moreover, the semantics encoded in STO allows for the discovery of relations between I4.0 standards, and for mappings across reference architectures proposed by different industrial communities.

Abstract While the Web was designed as a decentralised environment, individual authors still lack the ability to conveniently author and publish documents, and to engage in social interactions with documents of others in a truly decentralised fashion. We present dokieli , a fully decentralised, browser-based authoring and annotation platform with built-in support for social interactions, through which people retain ownership of and sovereignty over their data. The resulting “living” documents are interoperable and independent of dokieli since they follow standards and best practices, such as HTML+RDFa for a fine-grained semantic structure, Linked Data Platform for personal data storage, and Linked Data Notifications for updates. This article describes dokieli’s architecture and implementation, demonstrating advanced document authoring and interaction without a single point of control. Such an environment provides the right technological conditions for independent publication of scientific articles, news, and other works that benefit from diverse voices and open interactions. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/dokieli-rww .

Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.

Visualizations of ontologies, in particular graph visualizations in the form of node-link diagrams, are often used to support ontology development, exploration, verification, and sensemaking. With growing size and complexity of ontology graph visualizations, their represented information tend to become hard to comprehend due to visual clutter and information overload. We present a new approach of semantic zooming for ontology graph visualizations that abstracts and simplifies the underlying graph structure. It separates the comprised information into three layers with discrete levels of detail. The approach is applied to a force-directed graph layout using the VOWL notation. The mental map is preserved by using smart expanding and ordering of elements in the layout. Navigation and sensemaking are supported by local and global exploration methods, halo visualization, and smooth zooming. The results of a user study confirm an increase in readability, visual clarity, and information clarity of ontology graph visualizations enhanced with our semantic zooming approach.

Large collections of sensor data are semantically described using ontologies, e.g., the Semantic Sensor Network (SSN) ontology. Semantic sensor data are RDF descriptions of sensor observations from related sampling frames or sensors at multiple points in time, e.g., climate sensor data. Sensor values can be repeated in a sampling frame, e.g., a particular temperature value can be repeated several times, resulting in a considerable increase in data volume. We devise a factorized compact representation of semantic sensor data using linked data technologies to reduce repetition of same sensor values, and propose algorithms to generate collections of factorized semantic sensor data that can be managed by existing RDF triple stores. We empirically study the effectiveness of the proposed factorized representation of semantic sensor data. We show that the size of semantic sensor data is reduced by more than 50% on average without loss of information. Further, we have evaluated the impact of this factorized representation of semantic sensor data on query execution. Results suggest that query optimizers can be empowered with semantics from factorized representations to generate query plans that effectively speed up query execution time on factorized semantic sensor data.

Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer natural language questions using facts contained in a knowledge graph. Simple questions, which can be answered by the extraction of a single fact, constitute a large part of questions asked on the web but still pose challenges to QA systems, especially when asked against a large knowledge resource. Existing QA systems usually rely on various components each specialised in solving different sub-tasks of the problem (such as segmentation, entity recognition, disambiguation, and relation classification etc.). In this work, we follow a quite different approach: We train a neural network for answering simple questions in an end-to-end manner, leaving all decisions to the model. It learns to rank subject-predicate pairs to enable the retrieval of relevant facts given a question. The network contains a nested word/character-level question encoder which allows to handle out-of-vocabulary and rare word problems while still being able to exploit word-level semantics. Our approach achieves results competitive with state-of-the-art end-to-end approaches that rely on an attention mechanism.

Industry 4.0 is a global endeavor of automation and data exchange to create smart factories maximizing production capabilities and allowing for new business models. The Reference Architecture Model for Industry 4.0 (RAMI 4.0) describes the core aspects of Industry 4.0 and defines Administration Shells as digital representations of Industry 4.0 components. In this paper, we present an approach to model and implement Industry 4.0 components with the Resource Description Framework (RDF). The approach addresses the challenges of interoperable communication and machine comprehension in Industry 4.0 settings using semantic technologies. We show how related standards and vocabularies, such as IEC 62264, eCl@ss, and the Ontology of Units of Measure (OM), can be utilized along with the RDF-based representation of the RAMI 4.0 concepts. Finally, we demonstrate the applicability and benefits of the approach using an example from a real-world use case.

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Unification and automation of RESTful web services' documentation and descriptions is currently receiving increasing attention. The open-source OpenAPI Specification (formerly known as Swagger) has become core of this effort and has been adopted by a number of major companies. It allows the description of RESTful web services using objects represented in JSON or YAML file formats. As a result, the created descriptions are human and machine-readable, but not machine-understandable. In this paper, we propose a nonintrusive approach for the addition of semantic annotations (similar to RDFa and JSON-LD for HTML) to specific fields of the OpenAPI Specification. We created a lightweight vocabulary for describing RESTful web services using this specification. Furthermore, we practically demonstrate how OpenAPI objects can be enriched with semantic descriptions in a minimally invasive way by adding URIs in the values of chosen OpenAPI properties.

With the increasing permeation of data into all dimensions of our information society, data is progressively becoming the basis for many products and services. It is hence becoming more and more vital to identify the means and methods how to exploit the value of this data. In this paper we provide our definition of the Data Value Network, where we specifically cater for non-tangible data products. We also propose a Demand and Supply Distribution Model with the aim of providing insight on how an entity can participate in the global data market by producing a data product, as well as a concrete implementation through the Demand and Supply as a Service. Through our contributions we project our vision of generating a new Economic Data Ecosystem that has the Web of Data as its core.

Semantic technologies in enterprises have recently received increasing attention from both the research and industrial side. The concept of Linked Enterprise Data (LED) describes a framework to incorporate benefits of semantic technologies into enterprise IT environments. However, LED still remains an abstract idea lacking a point of origin, i.e., station zero from which it comes to existence. In this paper we argue and demonstrate that Enterprise Knowledge Graphs (EKGs) might be considered as an embodiment of LED lifting corporate information management to a semantic level which ultimately allows for real artificial intelligence applications. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Although the concept of EKGs was not invented yesterday, both enterprise and semantic communities have not yet come up with a formal comprehensive framework for designing such graphs. In this paper we aim to join the dots between the expanding interest in EKGs expressed by those communities and the lack of blueprints for realizing the EKGs. A thorough study of the key design concepts provides a multi-dimensional aspects matrix from which an enterprise is able to choose specific features of the highest priority. We emphasize the importance of various data fusion approaches, e.g., unified and federated. In the extensive evaluation section we investigate the effect of the chosen approach on the EKG performance along several dimensions, e.g., basic reasoning and OWL entailment which account for machine understanding of the EKG data, and access control subsystem which is of the utmost importance in large enterprises.

With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu. We evaluated its performance and accuracy on Linked Open Datasets of broad relevance.

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A vast amount of OpenCourseWare (OCW) is meanwhile being published online to make educational content accessible to larger audiences. The awareness of such courses among users and the popularity of systems providing such courses are increasing. However, from a subjective experience, OCW is frequently cursory, outdated or non-reusable. In order to obtain a better understanding of the quality of OCW, we assess the quality in terms of fitness for use. Based on three OCW use case scenarios, we define a range of dimensions according to which the quality of courses can be measured. From the definition of each dimension a comprehensive list of quality metrics is derived. In order to obtain a representative overview of the quality of OCW, we performed a quality assessment on a set of 100 randomly selected courses obtained from 20 different OCW repositories. Based on this assessment we identify crucial areas in which OCW needs to improve in order to deliver up to its promises.

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. We address this issue by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text.

Linked Open Data (LOD) comprises an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced or extracted data of often relatively low quality. We present a methodology for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. We argue that vocabularies, ontologies and knowledge bases should be accompanied by a number of test cases, which help to ensure a basic level of quality. We present a methodology for assessing the quality of linked data resources, based on a formalization of bad smells and data quality problems. Our formalization employs SPARQL query templates, which are instantiated into concrete quality test case queries. Based on an extensive survey, we compile a comprehensive library of data quality test case patterns. We perform automatic test case instantiation based on schema constraints or semi-automatically enriched schemata and allow the user to generate specific test case instantiations that are applicable to a schema or dataset. We provide an extensive evaluation of five LOD datasets, manual test case instantiation for five schemas and automatic test case instantiations for all available schemata registered with Linked Open Vocabularies (LOV). One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics.

Recently practical approaches for development of ubiquitous semantic applications have made quite some progress. In particular in the area of the ubiquitous access to the semantic data the authors recently observed a large number of approaches, systems and applications being described in the literature. With this survey the authors aim to provide an overview on the rapidly emerging field of Ubiquitous Semantic Applications (UbiSA). The authors conducted a systematic literature review comprising a thorough analysis of 48 primary studies out of 172 initially retrieved papers. The authors obtained a comprehensive set of quality attributes for UbiSA together with corresponding application features suggested for their realization. The quality attributes include aspects such as mobility, usability, heterogeneity, collaboration, customizability and evolvability. The primary studies were surveyed in the light of these quality attributes and the authors performed a thorough analysis of five ubiquitous semantic applications, six frameworks for UbiSA, three UbiSA specific ontologies, five ubiquitous semantic systems and nine general approaches. The proposed quality attributes facilitate the evaluation of existing approaches and the development of novel, more effective and intuitive UbiSA.

Recently, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.

The term Internet of Things refers to the vision, that all kinds of physical objects are uniquely identifiable and have a virtual representation on the Internet. We present an approach for equipping embedded and smart devices on the Internet of Things with a Linked Data interface. The approach is based on mapping existing structured data on the device to vocabularies and ontologies and exposing this information as dereferencable RDF directly from within the device. As a result, all smart devices (e.g. tablets, smartphones, TVs) can easily provide standardized structured information and become first class citizens on the Data Web. A particular specific requirement when dealing with smart and embedded devices is resource constraints. Our evaluation shows, that the overhead introduced by equipping a device with a Linked Data interface is neglectable given modern software and hardware environments.

The vast majority of the structured data of our age is stored in relational databases. In order to link and integrate this data on the Web, it is of paramount importance to map relational data to the RDF data model and make Linked Data interfaces to the data available. We can distinguish two main approaches: First, the database can be transformed into RDF row by row and the resulting knowledge base can be exposed using a triple store. Second, an RDB2RDF mapper performs SPARQL-to-SQL rewriting and thus exposes a virtual RDF graph based on the relational database. The key challenge of such a SPARQL-to-SQL rewriting is to create a SQL query which can be efficiently executed by the optimizer of the underlying relational database. In this article we discuss and evaluate the impact of different optimizations on query execution time using SparqlMap, a R2RML compliant SPARQL-to-SQL rewriter and compare the performance with state-of-the-art systems.

The Data Web contains a wealth of knowledge on a large number of domains. Question answering over interlinked data sources is challenging due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present a question answering system, which transforms user supplied queries (i.e. natural language sentences or keywords) into conjunctive SPARQL queries over a set of interlinked data sources. The contribution of this paper is two-fold: Firstly, we introduce a novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation). We employ a hidden Markov model, whose parameters were bootstrapped with different distribution functions. Secondly, we present a novel method for constructing a federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph which ultimately renders a corresponding SPARQL query. The results of our evaluation with three life-science datasets and 25 benchmark queries demonstrate the effectiveness of our approach.

The proliferation of Linked Open Data on the Web has increased the amount of data available for analysis and reuse. However, casual users find it difficult to explore and use Semantic Web Data due to the prevalence of specialised browsers that require complex queries to be formed and intimate knowledge on the structure of datasets. The authors address this problem in the Rhizomer tool by applying the data analysis mantra of overview, zoom and filter. These interaction patterns are implemented using information architecture components users are already familiar with but that are automatically generated from data and ontologies. This approach makes it possible to obtain an overview of the dataset being explored using techniques, such as navigation menus, treemaps or sitemaps, which are usually not available in text-based semantic web browsers. From there, users can interactively explore the data using facets. Moreover, facets also feature a pivoting operation, motivated during tests with lay users, that removes the main constraint of most faceted browsers, i.e. the inability to combine filters for differently faceted views to build complex queries.

The search for information on the Web of Data is becoming increasingly difficult due to its considerable growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal quer

A central component in many applications is the underlying data management layer. In Data-Web applications, the central component of this layer is the triple store. It is thus evident that finding the most adequate store for the application to develop is of crucial importance for individual projects as well as for data integration on the Data Web in general. In this paper, we propose a generic benchmark creation procedure for SPARQL, which we apply to the DBpedia knowledge base. In contrast to previous approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. In addition, our approach does not only take the query string but also the features of the queries into consideration during the benchmark generation process. Our generic procedure for benchmark creation is based on query-log mining, SPARQL feature analysis and clustering. After presenting the method underlying our benchmark generation algorithm, we use the generated benchmark to compare the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM.

Recently practical approaches for managing and supporting the life-cycle of semantic content on the Web of Data made quite some progress. However, the currently least developed aspect of the semantic content life-cycle is the user-friendly manual and semi-automatic creation of rich semantic content. In this paper we present the RDFaCE approach for combining WYSIWYG text authoring with the creation of rich semantic annotations. Our approach is based on providing four different views to the content authors: a classical WYSIWYG view, a WYSIWYM (What You See Is What You Mean) view making the semantic annotations visible, a fact view and the respective HTML/RDFa source code view. The views are synchronized such that changes made in one of the views automatically update the others. They provide different means of semantic content authoring for the different personas involved in the content creation life-cycle. For bootstrapping the semantic annotation process we integrate five different text annotation services. We evaluate their accuracy and empirically show that a combination of them yields superior results.

Despite the success of the emerging Linked Data Web, offering content in a machine-processable way and -- at the same time -- as a traditional Web site is still not a trivial task. In this paper, we present the OntoWiki-CMS - an extension to the collaborative knowledge engineering toolkit OntoWiki for managing semantically enriched Web content. OntoWiki-CMS is based on OntoWiki for the collaborative authoring of semantically enriched Web content, vocabularies and taxonomies for the semantic structuring of the Web content and the OntoWiki Site Extension, a template and dynamic syndication system for representing the semantically enriched content as a Web site and the dynamic integration of supplementary content. OntoWiki-CMS facilitates and integrates existing content-specific content management strategies (such as blogs, bibliographic repositories or social networks). OntoWiki-CMS helps to balance between the creation of rich, stable semantic structures and the participatory involvement of a potentially large editor and contributor community. As a result semantic structuring of the Web content facilitates better search, browsing and exploration as we demonstrate with a use case.

The search for information on the Web of Data is becoming increasingly difficult due to its dramatic growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e. g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, we present in this paper a novel approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to return instant answers to the user even with large knowledge bases.

It is widely accepted that there is a large disparity between the availability of treatment options and the prevalence of diseases all over the world, thus placing individuals in danger. This disparity is partially caused by the restricted access to information that would allow health care and research policy makers to formulate more appropriate measures to mitigate it. Specifically, this shortage of information is caused by the difficulty in reliably obtaining and integrating data regarding the disease burden and the respective research investments. In response to these challenges, the Linked Data paradigm provides a simple mechanism for publishing and interlinking structured information on the Web. In conjunction with the ever increasing data on diseases and health care research available as Linked Data, an opportunity is created to reduce this information gap that would allow for better policy in response to these disparities. In this paper, we present the ReDD-Observatory, an approach for evaluating the Research-Disease Disparity based on the interlinking and integrating of various biomedical data sources. Specifically, we devise a method for representing statistical information as Linked Data and adopt interlinking algorithms for integrating relevant datasets (mainly GHO, Linked CT and PubMed). The assessment of the disparity is then performed with a number of parametrized SPARQL queries on the integrated data substrate. As a consequence, we are for the first time able to provide reliable indicators for the extent of the research-disease disparity in a semi-automated fashion, thus enabling health care professionals and policy makers to make more informed decisions.

In this paper we present Triplify - a simplistic but effective approach to publish Linked Data from relational databases. Triplify is based on mapping HTTP-URI requests onto relational database queries. Triplify transforms the resulting relations into RDF statements and publishes the data on the Web in various RDF serializations, in particular as Linked Data. The rationale for developing Triplify is that the largest part of information on the Web is already stored in structured form, often as data contained in relational databases, but usually published by Web applications only as HTML mixing structure, layout and content. In order to reveal the pure structured information behind the current Web, we have implemented Triplify as a light-weight software component, which can be easily integrated into and deployed by the numerous, widely installed Web applications. Our approach includes a method for publishing update logs to enable incremental crawling of linked data sources. Triplify is complemented by a library of configurations for common relational schemata and a REST-enabled data source registry. Triplify configurations containing mappings are provided for many popular Web applications, including osCommerce, WordPress, Drupal, Gallery, and phpBB. We will show that despite its light-weight architecture Triplify is usable to publish very large datasets, such as 160GB of geo data from the OpenStreetMap project.

The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, the authors present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. They describe in detail how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Their algorithms are made available in the open source DL-Learner project and we present several real-life scenarios in which they can be used by Semantic Web applications.

Business applications are more and more often developed on the basis of Web services. The aim is to provide platform independence and loose coupling between business applications to facilitate distributed and grid computing scenarios. However, most efforts to deploy and publish Web services are manual. Manual discovery, invocation and composition of Web services in a distributed computing environment significantly hamper the automatic process of enterprise application integration. Semantic enhancements in Web services aim at making the process of Web services discovery, invocation and composition dynamic by exposing the machine understandable description of Web service capabilities and Web service requests. In this paper we compare recent dynamic Web service composition approaches. We highlight some dynamic composition issues and compare existing approaches with respect to these issues. Based on these findings we present a new and generic semantic Web services integration and composition lifecycle to facilitate the semantic based integration and composition of grid services. The proposed semantic Web services integration and composition life cycle explains the necessary integration phases beginning with the modeling and developing of processes as Web service composition and ending with their execution. With this lifecycle, integration hurdles among different service composition approaches will be diminished.